30 citations · 82 across the 10 of their papers we have counts for
10 papers
Resilient Control of Networked Microgrids using Vertical Federated Reinforcement Learning: Designs and Real-Time Test-Bed Validations
Sayak Mukherjee, Ramij R. Hossain, Sheik M. Mohiuddin +7
Improving system-level resiliency of networked microgrids is an important aspect with increased population of inverter-based resources (IBRs). This paper (1) presents resilient con…
Toward Intelligent Emergency Control for Large-scale Power Systems: Convergence of Learning, Physics, Computing and Control
Qiuhua Huang, Renke Huang, Tianzhixi Yin +13
This paper has delved into the pressing need for intelligent emergency control in large-scale power systems, which are experiencing significant transformations and are operating cl…
Enhancing Cyber Resilience of Networked Microgrids using Vertical Federated Reinforcement Learning
Sayak Mukherjee, Ramij R. Hossain, Yuan Liu +6
This paper presents a novel federated reinforcement learning (Fed-RL) methodology to enhance the cyber resiliency of networked microgrids. We formulate a resilient reinforcement le…
Efficient Learning of Voltage Control Strategies via Model-based Deep Reinforcement Learning
Ramij R. Hossain, Tianzhixi Yin, Yan Du +5
This article proposes a model-based deep reinforcement learning (DRL) method to design emergency control strategies for short-term voltage stability problems in power systems. Rece…
Physics-informed Evolutionary Strategy based Control for Mitigating Delayed Voltage Recovery
Yan Du, Qiuhua Huang, Renke Huang +4
In this work we propose a novel data-driven, real-time power system voltage control method based on the physics-informed guided meta evolutionary strategy (ES). The main objective…
Scalable Voltage Control using Structure-Driven Hierarchical Deep Reinforcement Learning
Sayak Mukherjee, Renke Huang, Qiuhua Huang +2
This paper presents a novel hierarchical deep reinforcement learning (DRL) based design for the voltage control of power grids. DRL agents are trained for fast, and adaptive select…
Learning and Fast Adaptation for Grid Emergency Control via Deep Meta Reinforcement Learning
Renke Huang, Yujiao Chen, Tianzhixi Yin +6
As power systems are undergoing a significant transformation with more uncertainties, less inertia and closer to operation limits, there is increasing risk of large outages. Thus,…
Accelerated Deep Reinforcement Learning Based Load Shedding for Emergency Voltage Control
Renke Huang, Yujiao Chen, Tianzhixi Yin +6
Load shedding has been one of the most widely used and effective emergency control approaches against voltage instability. With increased uncertainties and rapidly changing operati…
Parameters Calibration for Power Grid Stability Models using Deep Learning Methods
Renke Huang, Rui Fan, Tianzhixi Yin +2
This paper presents a novel parameter calibration approach for power system stability models using automatic data generation and advanced deep learning technology. A PMU-measuremen…
Convolutional Neural Network and Transfer Learning for High Impedance Fault Detection
Rui Fan, Tianzhixi Yin
This letter presents a novel high impedance fault (HIF) detection approach using a convolutional neural network (CNN). Compared to traditional artificial neural networks, a CNN off…